Moving forward on the science of informatics and predictive analytics.
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocae077
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocae077
Development of clinical phenotypes from electronic health records (EHRs) can be resource intensive. Several phenotype libraries have been created to facilitate reuse of definitions. However, these platforms vary in target audience and utility. We describe the development of the Centralized Interactive Phenomics Resource (CIPHER) knowledgebase, a comprehensive public-facing phenotype library, which aims to facilitate clinical and health services research.
Author(s): Honerlaw, Jacqueline, Ho, Yuk-Lam, Fontin, Francesca, Murray, Michael, Galloway, Ashley, Heise, David, Connatser, Keith, Davies, Laura, Gosian, Jeffrey, Maripuri, Monika, Russo, John, Sangar, Rahul, Tanukonda, Vidisha, Zielinski, Edward, Dubreuil, Maureen, Zimolzak, Andrew J, Panickan, Vidul A, Cheng, Su-Chun, Whitbourne, Stacey B, Gagnon, David R, Cai, Tianxi, Liao, Katherine P, Ramoni, Rachel B, Gaziano, J Michael, Muralidhar, Sumitra, Cho, Kelly
DOI: 10.1093/jamia/ocae042
To solve major clinical natural language processing (NLP) tasks using a unified text-to-text learning architecture based on a generative large language model (LLM) via prompt tuning.
Author(s): Peng, Cheng, Yang, Xi, Chen, Aokun, Yu, Zehao, Smith, Kaleb E, Costa, Anthony B, Flores, Mona G, Bian, Jiang, Wu, Yonghui
DOI: 10.1093/jamia/ocae078
With its size and diversity, the All of Us Research Program has the potential to power and improve representation in clinical trials through ancillary studies like Nutrition for Precision Health. We sought to characterize high-level trial opportunities for the diverse participants and sponsors of future trial investment.
Author(s): Shyr, Cathy, Sulieman, Lina, Harris, Paul A
DOI: 10.1093/jamia/ocae062
Recently, large language models (LLMs) have showcased remarkable capabilities in natural language understanding. While demonstrating proficiency in everyday conversations and question-answering (QA) situations, these models frequently struggle in domains that require precision, such as medical applications, due to their lack of domain-specific knowledge. In this article, we describe the procedure for building a powerful, open-source language model specifically designed for medicine applications, termed as PMC-LLaMA.
Author(s): Wu, Chaoyi, Lin, Weixiong, Zhang, Xiaoman, Zhang, Ya, Xie, Weidi, Wang, Yanfeng
DOI: 10.1093/jamia/ocae045
Phenotyping is a core task in observational health research utilizing electronic health records (EHRs). Developing an accurate algorithm demands substantial input from domain experts, involving extensive literature review and evidence synthesis. This burdensome process limits scalability and delays knowledge discovery. We investigate the potential for leveraging large language models (LLMs) to enhance the efficiency of EHR phenotyping by generating high-quality algorithm drafts.
Author(s): Yan, Chao, Ong, Henry H, Grabowska, Monika E, Krantz, Matthew S, Su, Wu-Chen, Dickson, Alyson L, Peterson, Josh F, Feng, QiPing, Roden, Dan M, Stein, C Michael, Kerchberger, V Eric, Malin, Bradley A, Wei, Wei-Qi
DOI: 10.1093/jamia/ocae072
Firearm violence constitutes a public health crisis in the United States, but comprehensive data infrastructure is lacking to study this problem. To address this challenge, we used natural language processing (NLP) to classify court record documents from alleged violent crimes as firearm-related or non-firearm-related.
Author(s): Kafka, Julie M, Schleimer, Julia P, Toomet, Ott, Chen, Kaidi, Ellyson, Alice, Rowhani-Rahbar, Ali
DOI: 10.1093/jamia/ocae082
We conducted an implementation planning process during the pilot phase of a pragmatic trial, which tests an intervention guided by artificial intelligence (AI) analytics sourced from noninvasive monitoring data in heart failure patients (LINK-HF2).
Author(s): Sideris, Konstantinos, Weir, Charlene R, Schmalfuss, Carsten, Hanson, Heather, Pipke, Matt, Tseng, Po-He, Lewis, Neil, Sallam, Karim, Bozkurt, Biykem, Hanff, Thomas, Schofield, Richard, Larimer, Karen, Kyriakopoulos, Christos P, Taleb, Iosif, Brinker, Lina, Curry, Tempa, Knecht, Cheri, Butler, Jorie M, Stehlik, Josef
DOI: 10.1093/jamia/ocae017
Stressful life events, such as going through divorce, can have an important impact on human health. However, there are challenges in capturing these events in electronic health records (EHR). We conducted a scoping review aimed to answer 2 major questions: how stressful life events are documented in EHR and how they are utilized in research and clinical care.
Author(s): Scherbakov, Dmitry, Mollalo, Abolfazl, Lenert, Leslie
DOI: 10.1093/jamia/ocae023
Deep-learning techniques, particularly the Transformer model, have shown great potential in enhancing the prediction performance of longitudinal health records. Previous methods focused on fixed-time risk prediction, however, time-to-event prediction is often more appropriate for clinical scenarios. Here, we present STRAFE, a generalizable survival analysis Transformer-based architecture for electronic health records.
Author(s): Zisser, Moshe, Aran, Dvir
DOI: 10.1093/jamia/ocae025